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This work presents machine learning-inspired data fusion approaches to improve the non-destructive testing of reinforced concrete. The principal effects that are used for data fusion are shown theoretically. Their effectiveness is tested in case studies carried out on largescale concrete specimens with built-in chloride-induced rebar corrosion. The dataset consists of half-cell potential mapping, Wenner resistivity, microwave moisture and ground penetrating radar measurements. Data fusion is based on the logistic Regression algorithm.
It learns an optimal linear decision boundary from multivariate labeled training data, to separate intact and defect areas. The training data are generated in an experiment that simulates the entire life cycle of chloride-exposed concrete building parts. The unique possibility to monitor the deterioration, and targeted corrosion initiation, allows data labeling.
The results exhibit an improved sensitivity of the data fusion with logistic regression compared to the best individual method half-cell potential.
An important aspect with regard to the service life of zinc based galvanic anodes and the durability of the corrosion protection of steel in concrete is the “galvanic chloride extraction”. Chloride ions move in the electric field generated by the current, flowing between the galvanic anode and the cathodic steel. Migration leads to an accumulation of anions, e.g. chloride ions, at the anode and depletion of chlorides near the steel rebar surface. The ion migration was studied on steel reinforced concrete specimens admixed with 3 wt.% chloride/wt. cement and galvanically protected by a surface applied embedded zinc anode (EZA). The zinc anode was embedded and glued to the concrete surface by a geo-polymer based chloride free binder. The EZA was operated over a period of 1 year and the ion distribution between anode (EZA) and cathode (steel reinforcement) was studied by laser induced breakdown spectroscopy (LIBS) after 5 months, 7 months and 12 months. The results show that chloride ions efficiently migrate in the direction of the zinc-anode and accumulate there. Chloride distribution in the EZA correlates with the distribution of zinc ions generated by the anodic dissolution of the zinc anode in the binder matrix. The microstructure of the binder matrix and its interface to the zinc-anode are studied by REM/EDX – preliminary results will be reported.